xintongsong commented on a change in pull request #12865:
URL: https://github.com/apache/flink/pull/12865#discussion_r457283968
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File path: docs/ops/external_resources.zh.md
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@@ -23,98 +23,90 @@ specific language governing permissions and limitations
under the License.
-->
-In addition to CPU and memory, many workloads also need some other resources,
e.g. GPUs for deep learning. To support external
-resources, Flink provides an external resource framework. The framework
supports requesting various types of resources from the
-underlying resource management systems (e.g., Kubernetes), and supplies
information needed for using these resources to the operators.
-Different resource types can be supported. You can either leverage built-in
plugins provided by Flink (currently only for GPU support),
-or implement your own plugins for custom resource types.
+许多计算任务需要使用除了 CPU 与内存外的资源，如用深入学习场景需要使用 GPU 来进行加速。为了支持这种扩展资源，Flink 提供了一
+个扩展资源框架。该框架支持从底层资源管理系统（如 Kubernetes）请求各种类型的资源，并向 Operator 提供使用这些资源所需的信息。该框架
+以插件形式支持不同的资源类型。目前 Flink 仅内置了支持 GPU 资源的插件，你可以为你想使用的资源类型实现第三方插件。
* This will be replaced by the TOC
{:toc}
-# What the external resource framework does
+<a name="what-the-external-resource-framework-does"></a>
-In general, the external resource framework does two things:
+# 扩展资源框架做了什么
- - Set the corresponding fields of the resource requests (for requesting
resources from the underlying system) with respect to your configuration.
+External resource 框架主要做了以下两件事：
- - Provide operators with the *information* needed for using the resources.
+ - 根据你的配置，在 Flink 从底层资源管理系统中申请资源时，设置与扩展资源相关的字段
-When deployed on resource management systems (Kubernetes / Yarn), the external
resource framework will ensure that the allocated pod/container
-will contain the desired external resources. Currently, many resource
management systems support external resources. For example,
-Kubernetes supports GPU, FPGA, etc. through its [Device
Plugin](https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/)
-mechanism since v1.10, and Yarn supports GPU and FPGA resources since 2.10 and
3.1. External resources are not supported by Flink’s Mesos
-integration at the moment. In Standalone mode, the user has to ensure that the
external resources are available.
+ - 为 Operator 提供使用这些资源所需要的*信息*
-The external resource framework will provide the corresponding *information*
to operators. The external resource information,
-which contains the basic properties needed for using the resources, is
generated by the configured external resource *drivers*.
+当 Flink 部署在资源管理系统（Kubernetes、Yarn）上时，扩展资源框架将确保分配的 Pod、Container
包含所需的扩展资源。目前，许多资源
+管理系统都支持扩展资源。例如，Kubernetes 从 v1.10 开始通过 [Device
Plugin](https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/)
机制
+支持 GPU、FPGA 等资源调度，Yarn 从 2.10 和 3.1 开始支持 GPU 和 FPGA 的调度。目前，扩展资源框架并不支持 Mesos
模式。在 Standalone 模式下，由用户负责确保扩展资源的可用性。
-# Enable the external resource framework for your workload
+扩展资源框架向 Operator 提供扩展资源相关*信息*，这些信息由你配置的扩展资源 *Driver* 生成，包含了使用扩展资源所需要的基本属性。
-To enable an external resource with the external resource framework, you need
to:
+<a name="enable-the-external-resource-framework-for-your-workload"></a>
- - Prepare the external resource *plugin*.
+# 启用扩展资源框架
- - Set configurations for the external resource.
+为了启用扩展资源框架来使用扩展资源，你需要：
- - Get the external resource *information* from `RuntimeContext` and use it
in your operators.
+ - 为该扩展资源准备扩展资源框架的*插件*
-## Prepare plugins
+ - 为该扩展资源设置相关的配置
-You need to prepare the external resource plugin and put it into the
`plugins/` folder of your Flink distribution, see
-[Flink Plugins]({% link ops/plugins.zh.md %}). Apache Flink provides a
first-party [plugin for GPU resources](#plugin-for-gpu-resources). You can also
-[implement a plugin for your custom resource
type](#implement-a-plugin-for-your-custom-resource-type).
+ - 在你的 Operator 中，从 `RuntimeContext` 来获取扩展资源的*信息*并使用这些资源
-## Configurations
+<a name="prepare-plugins"></a>
-First, you need to add resource names for all the external resource types to
the **external resource list (with the configuration key ‘external-resources’)**
-with delimiter ";", e.g. "external-resources: gpu;fpga" for two external
resources "gpu" and "fpga". Only the **\<resource_name\>**
-defined here will go into effect in the external resource framework.
+## 准备插件
-For each external resource, you could configure the below options. The
**\<resource_name\>** in all the below configuration options
-corresponds to the name listed in the **external resource list**:
+你需要为使用的扩展资源准备插件，并将其放入 Flink 发行版的 `plugins/` 文件夹中, 参看 [Flink Plugins]({% link
ops/plugins.zh.md %})。
+Flink 提供了第一方的 [GPU
资源插件](#plugin-for-gpu-resources)。你同样可以为你所使用的扩展资源实现自定义插件[实现自定义插件](#implement-a-plugin-for-your-custom-resource-type)。
- - **Amount** (`external.<resource_name>.amount`): This is the quantity of
the external resource that should be requested from the external system.
+## 配置项
- - **Config key in Yarn**
(`external-resource.<resource_name>.yarn.config-key`): *optional*. If
configured, the external
- resource framework will add this key to the resource profile of container
requests for Yarn. The value will be set to the
- value of `external-resource.<resource_name>.amount`.
+首先，你需要使用分隔符“;”将所有使用的扩展资源类型的资源名称添加到 **扩展资源列表（配置键“external-resources”）**
中，例如，“external-resources: gpu;fpga”定义了两个扩展资源“gpu”和“fpga”。
+只有此处定义了扩展资源名称（**\<resource_name\>**），相应的资源才会在扩展资源框架中生效。
- - **Config key in Kubernetes**
(`external-resource.<resource_name>.kubernetes.config-key`): *optional*. If
configured,
- external resource framework will add `resources.limits.<config-key>` and
`resources.requests.<config-key>` to the main
- container spec of TaskManager and set the value to the value of
`external-resource.<resource_name>.amount`.
+对于每个扩展资源，有以下配置选项。下面的所有配置选项中的 **\<resource_name\>** 对应于 **扩展资源列表** 中列出的名称：
- - **Driver Factory**
(`external-resource.<resource_name>.driver-factory.class`): *optional*. Defines
the factory class
Review comment:
Ok, that makes sense. I would suggest to explain this for both languages.
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